A sentiment analysis software framework for the support of business information architecture in the tourist sector

In recent years, the increased use of digital tools within the Peruvian tourism industry has created a corresponding increase in revenues. However, both factors have caused increased competition in the sector that in turn puts pressure on small and medium enterprises' (SME) revenues and profitability. This study aims to apply neural network based sentiment analysis on social networks to generate a new information search channel that provides a global understanding of user trends and preferences in the tourism sector. A working data-analysis framework will be developed and integrated with tools from the cloud to allow a visual assessment of high probability outcomes based on historical data, to help SMEs estimate the number of tourists arriving and places they want to visit, so that they can generate desirable travel packages in advance, reduce logistics costs, increase sales, and ultimately improve both quality and precision of customer service

Emotional Concept Extraction Through Ontology-Enhanced Classification

Capturing emotions affecting human behavior in social media bears strategic importance in many decision-making fields, such as business and public policy, health care, and financial services, or just social events. This paper introduces an emotion-based classification model to analyze the human behavior in reaction to some event described by a tweet trend. From tweets analysis, the model extracts terms expressing emotions, and then, it builds a topological space of emotion-based concepts. These concepts enable the training of the multi-class SVM classifier to identify emotions expressed in the tweets. Classifier results are “softly” interpreted as a blending of several emotional nuances which thoroughly depicts people’s feeling. An ontology model captures the emotional concepts returned by classification, with respect to the tweet trends. The associated knowledge base provides human behavior analysis, in response to an event, by a tweet trend, by SPARQL queries. © 2019, Springer Nature Switzerland AG